Over 60% of cancers occur in older persons, and the number of older cancer patients is expected to grow as the population ages. Older cancer patients are at increased risk of chemotherapy treatment complications, and there is no standard approach to identifying risk and implementing interventions to reduce toxicity. Several studies, including a Cancer and Aging Research Group (CARG) study in 500 patients, have demonstrated that 46% of older patients with advanced cancer have severe toxicity from chemotherapy within 3 months of treatment initiation and that comprehensive geriatric assessment (GA), a validated approach to assessing health status in older persons, can predict severe chemotherapy toxicities. No consensus exists, however, on how to translate information from the GA into targeted interventions that have potential to prevent or reduce adverse outcomes (e.g., toxicity, functional decline, and lower quality of life) for older cancer patients. The overarching goal of this proposal is to identify geriatric assessment (GA)-driven interventions that could improve outcomes of older adults with cancer, to create an algorithm to implement these interventions, and to test the algorithm in a randomized pilot study. The overall hypothesis of this proposed research is that an algorithm that implements interventions guided by GA for older patients can reduce the risk of chemotherapy toxicity and maintain or improve quality of life. The principal investigator, a geriatric oncologist, and the research team of geriatricians, oncologists, and geriatric oncologists assembled through CARG and a funded U13 conference series grant, Geriatric Oncology Research to Improve Clinical Care are well positioned to successfully complete this high-impact research. In specific aim 1, we will employ a modified Delphi consensus-building process with experts convened at the second U13 geriatric oncology conference to revise and finalize an algorithm for GA-driven interventions for cancer patients. In specific aim 2, we will conduct a randomized pilot study (n=80) to gather preliminary data on whether this algorithm for GA-driven interventions can improve outcomes of older cancer patients. Patients aged 70 and over with advanced cancer who are embarking on a new chemotherapy regimen will be randomized to usual care or a visit with a geriatric oncology team who will implement GA-driven interventions guided by the algorithm. The primary outcome will be a comparison of the proportion of patients who have severe chemotherapy toxicity at 3 months after chemotherapy initiation. Secondary outcomes will include a comparison of the number of interventions implemented in both groups and comparisons of quality of life, functional abilities, and physical performance. The CARG research team, led by the PI, will incorporate the results of this proposal in a planned R01 within the University of Rochester's Community Clinical Oncology Program (CCOP) that will test whether implementation of GA-driven interventions can reduce toxicity and maintain quality of life of older cancer patients receiving chemotherapy.